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LICENSE.md
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# LICENSE
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## 1. Model & License Summary
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This repository contains **JAM** (the "Model"), the first open-sourced model released under **Project Jamify**, developed for facilitating academic research and creative exploration in AI-generated songs from lyrics. The Model is subject to:
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1. The **Project Jamify License**: Intended **solely for non-commercial, academic, and entertainment purposes**
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2. The **Stability AI Community License Agreement**, provided in the file ```STABILITY_AI_COMMUNITY_LICENSE.md```.
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By using or distributing this Model, you **agree** to adhere to all applicable licenses and restrictions, as summarized below.
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---
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## 2. Project Jamify License Terms
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**JAM** is developed with the primary objective of facilitating academic research and creative exploration in AI-generated songs from lyrics. We emphasize the following:
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- **No copyrighted material** was used in a way that would intentionally infringe on intellectual property rights. JAM is not designed to reproduce or imitate any specific artist, label, or protected work.
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- Outputs generated by JAM must **not be used to create or disseminate content that violates copyright laws**.
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- The **commercial use of JAM or its outputs is strictly prohibited**.
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- Responsibility for the use of the model and its outputs lies entirely with the end user, who must ensure all uses comply with applicable legal and ethical standards.
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---
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## 3. Stability AI Community License Requirements
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- You must comply with the **Stability AI Community License Agreement** (the "Agreement") for any usage, distribution, or modification of this Model.
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- **Non-Commercial Use**: This Model is for research and academic purposes only. Any commercial usage requires registering with Stability AI or obtaining a separate commercial license.
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- **Attribution & Notice**:
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- Retain the notice:
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```
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This Stability AI Model is licensed under the Stability AI Community License, Copyright Β© Stability AI Ltd. All Rights Reserved.
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```
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- Clearly display "Powered by Stability AI" if you build upon or showcase this Model.
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- **Disclaimer & Liability**: This Model is provided **"AS IS"** with **no warranties**. Neither we nor Stability AI will be liable for any claim or damages related to Model use.
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See ```STABILITY_AI_COMMUNITY_LICENSE.md``` for the full text.
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---
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## 4. UK Data Copyright Exemption
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This Model was developed under the **UK data copyright exemption for non-commercial research**. Distribution or use outside these bounds must **not** violate that exemption or infringe on any underlying dataset's license.
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---
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## 5. Further Information
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- **Stability AI License Terms**: <https://stability.ai/community-license>
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For questions, concerns, or collaboration inquiries, please contact the Project Jamify team via the official repository or project website.
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---
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**End of License**.
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README.md
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# JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment
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JAM is a rectified flow-based model for lyrics-to-song generation that addresses the lack of fine-grained word-level controllability in existing lyrics-to-song models. Built on a compact 530M-parameter architecture with 16 LLaMA-style Transformer layers as the Diffusion Transformer (DiT) backbone, JAM enables precise vocal control that musicians desire in their workflows. Unlike previous models, JAM provides word and phoneme-level timing control, allowing musicians to specify the exact placement of each vocal sound for improved rhythmic flexibility and expressive timing.
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## Features
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- **Fine-grained Word and Phoneme-level Timing Control**: The first model to provide word-level timing and duration control in song generation, enabling precise prosody control for musicians
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- **Compact 530M Parameter Architecture**: Less than half the size of existing models, enabling faster inference with reduced resource requirements
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- **Enhanced Lyric Fidelity**: Achieves over 3Γ reduction in Word Error Rate (WER) and Phoneme Error Rate (PER) compared to prior work through precise phoneme boundary attention
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- **Global Duration Control**: Controllable duration up to 3 minutes and 50 seconds.
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- **Aesthetic Alignment through Direct Preference Optimization**: Iterative refinement using synthetic preference datasets to better align with human aesthetic preferences, eliminating manual annotation requirements
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## JAM Samples
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Check out the example generated music in the `generated_examples/` folder to hear what JAM can produce:
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- **`Hybrid Minds, Brodie - Heroin.mp3`** - Electronic music with synthesized beats and electronic elements
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- **`Jade Bird - Avalanche.mp3`** - Country music with acoustic guitar and folk influences
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- **`Rizzle Kicks, Rachel Chinouriri - Follow Excitement!.mp3`** - Rap music with rhythmic beats and hip-hop style
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These samples demonstrate JAM's ability to generate high-quality music across different genres while maintaining vocal intelligence, style consistency and musical coherence.
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## Requirements
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- Python 3.10 or higher
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- CUDA-compatible GPU with sufficient VRAM (8GB+ recommended)
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## Installation
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### 1. Clone the Repository
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```bash
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git clone <repository-url>
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cd jam
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```
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### 2. Run Installation Script
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The project includes an automated installation script, run it in your own virtual environment:
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```bash
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bash install.sh
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```
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This script will:
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- Initialize and update git submodules (DeepPhonemizer)
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- Install Python dependencies from `requirements.txt`
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- Install the JAM package in editable mode
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- Install the DeepPhonemizer external dependency
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### 3. Manual Installation (Alternative)
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If you prefer manual installation:
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```bash
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# Initialize submodules
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git submodule update --init --recursive
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# Install dependencies
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pip install -r requirements.txt
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# Install JAM package
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pip install -e .
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# Install DeepPhonemizer
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pip install -e externals/DeepPhonemizer
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```
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## Quick Start
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### Simple Inference
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The easiest way to run inference is using the provided `inference.py` script:
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```python
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python inference.py
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```
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This script will:
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1. Download the pre-trained JAM-0.5 model from Hugging Face
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2. Run inference with default settings
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3. Save generated audio to the `outputs` directory
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### Input Format
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Create an input file at `inputs/input.json` with your songs:
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```json
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[
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{
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"id": "my_song",
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"audio_path": "inputs/reference_audio.mp3",
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"lrc_path": "inputs/lyrics.json",
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"duration": 180.0,
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"prompt_path": "inputs/style_prompt.txt"
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}
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]
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```
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Required files:
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- **Audio file**: Reference audio for style extraction
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- **Lyrics file**: JSON with timestamped lyrics
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- **Prompt file**: Text description of desired style/genre. Text prompt is not used in the default setting where the audio reference is utilized.
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## Advanced Usage
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### Using `python -m jam.infer`
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For more control over the generation process:
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```bash
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# Basic usage with custom checkpoint
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python -m jam.infer evaluation.checkpoint_path=path/to/model.safetensors
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# With custom output directory
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python -m jam.infer evaluation.checkpoint_path=path/to/model.safetensors evaluation.output_dir=my_outputs
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# With custom configuration file
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python -m jam.infer config=configs/my_config.yaml evaluation.checkpoint_path=path/to/model.safetensors
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```
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### Multi-GPU Inference
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Use Accelerate for distributed inference:
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```bash
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# Basic usage with custom checkpoint
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accelerate launch --config_path path/to/accelerate/config.yaml jam.infer
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# With custom configuration file
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accelerate launch --config_path path/to/accelerate/config.yaml jam.infer config=path/to/inference/config.yaml
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```
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## Configuration Options
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### Key Parameters
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#### Evaluation Settings
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- `evaluation.checkpoint_path`: Path to model checkpoint (required)
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- `evaluation.output_dir`: Output directory (default: "outputs")
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- `evaluation.test_set_path`: Input JSON file (default: "inputs/input.json")
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- `evaluation.batch_size`: Batch size for inference (default: 1)
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- `evaluation.num_samples`: Only generate first n samples in test_set_path (null = all)
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- `evaluation.vae_type`: VAE model type ("diffrhythm" or "stable_audio")
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#### Style Control
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- `evaluation.ignore_style`: Ignore style prompts (default: false)
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- `evaluation.use_prompt_style`: Use text prompts for style (default: false)
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- `evaluation.num_style_secs`: Style audio duration in seconds (default: 30)
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- `evaluation.random_crop_style`: Randomly crop style audio (default: false)
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## Input File Formats
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### Lyrics File (`*.json`)
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```json
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[
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{"start": 2.2, "end": 2.5, "word": "First word of lyrics"},
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{"start": 2.5, "end": 3.7, "word": "Second word of lyrics"},
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{"more lines ...."}
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]
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```
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### Style Prompt File (`*.txt`)
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```
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Electronic dance music with heavy bass and synthesizers
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```
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### Input Manifest (`input.json`)
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```json
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[
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{
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"id": "unique_song_id",
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"audio_path": "path/to/reference.mp3",
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"lrc_path": "path/to/lyrics.json",
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"duration": 180.0,
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"prompt_path": "path/to/style.txt"
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}
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]
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```
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## Output Structure
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Generated files are saved to the output directory:
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```
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outputs/
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βββ generated/ # Final trimmed audio files
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βββ generated_orig/ # Original generated audio
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βββ cfm_latents/ # Intermediate latent representations
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βββ local_files/ # Process-specific metadata
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βββ generation_config.yaml # Configuration used for generation
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```
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## Performance Tips
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1. **GPU Memory**: Use `evaluation.batch_size=1` for large on limited VRAM
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2. **Multi-GPU**: Use `accelerate launch` for faster processing of multiple samples
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3. **Mixed Precision**: Add `--mixed_precision=fp16` to reduce memory usage
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## Troubleshooting
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### Common Issues
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#### "Checkpoint path not found"
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```bash
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# Make sure to specify the checkpoint path
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python -m jam.infer evaluation.checkpoint_path=path/to/your/model.safetensors
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```
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#### "CUDA out of memory"
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```bash
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# Reduce batch size or use mixed precision
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accelerate launch --mixed_precision=fp16 -m jam.infer evaluation.checkpoint_path=model.safetensors
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```
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#### "Test set not found"
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```bash
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# Create input.json file in inputs/ directory or specify custom path
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python -m jam.infer evaluation.test_set_path=path/to/your/input.json evaluation.checkpoint_path=model.safetensors
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```
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## Model Downloads
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The `inference.py` script automatically downloads the JAM-0.5 model. For manual download:
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```python
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from huggingface_hub import snapshot_download
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model_path = snapshot_download(repo_id="declare-lab/jam-0.5")
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```
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## Citation
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If you use JAM in your research, please cite:
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```bibtex
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@misc{jam2024,
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title={JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment},
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author={Renhang Liu and Chia-Yu Hung and Navonil Majumder and Taylor Gautreaux and Amir Ali Bagherzadeh and Chuan Li and Dorien Herremans and Soujanya Poria},
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year={2025}
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}
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```
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+
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## License
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245 |
+
|
246 |
+
**JAM** is the first open-sourced model released under **Project Jamify**, developed for facilitating academic research and creative exploration in AI-generated songs from lyrics. The model is subject to:
|
247 |
+
|
248 |
+
1. **Project Jamify License**: Intended **solely for non-commercial, academic, and entertainment purposes**
|
249 |
+
2. **Stability AI Community License Agreement**: Required due to use of Stability AI model components
|
250 |
+
|
251 |
+
### Key Restrictions:
|
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- **No copyrighted material** was used in a way that would intentionally infringe on intellectual property rights
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- **JAM is not designed** to reproduce or imitate any specific artist, label, or protected work
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- Outputs generated by JAM must **not be used to create or disseminate content that violates copyright laws**
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- **Commercial use of JAM or its outputs is strictly prohibited**
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- **Attribution Required**: Must retain "This Stability AI Model is licensed under the Stability AI Community License, Copyright Β© Stability AI Ltd. All Rights Reserved."
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### Responsibility
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Responsibility for the use of the model and its outputs lies entirely with the end user, who must ensure all uses comply with applicable legal and ethical standards.
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For complete license terms, see [LICENSE.md](LICENSE.md) and [STABILITY_AI_COMMUNITY_LICENSE.md](STABILITY_AI_COMMUNITY_LICENSE.md).
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For questions, concerns, or collaboration inquiries, please contact the Project Jamify team via the official repository.
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## Support
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For issues and questions:
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- Open an issue on GitHub
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- Check the troubleshooting section above
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- Review the configuration options for parameter tuning
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